R-bloggers
Ride sharing is a popular way to get around and much cheaper than some alternatives. How much do the people who choose to participate in ride sharing actually make? We will start this series of examining Lyft driving data by getting the data loaded and trying to understand which hours are the best to drive.
The first step is to get our data loaded. The data is contained in a Github repository . You can clone the repo to get started or just directly read in the csv file. We will be just read in the data file directly. If you want to see the whole script in one place, try LyftData.R .
# Load libraries ---- library(lubridate) library(sqldf) library(skimr) # Read in Data --- lyft <- read.csv(file="https://raw.githubusercontent.com/Spoted21/lyft/master/lyft2.csv", stringsAsFactors = FALSE) # Examine Data ---- head(lyft) dim(lyft) str(lyft) # My new favorite function skim(lyft) After getting the data loaded there is some cleaning that is needed. After cleaning the data we will be looking at which hours are the best to drive. It will be useful to create a variable to indicate which rides are
R-bloggers